Diagnosis, descpipton of the process for doctors

Diagnosis, descpipton of the process for doctors

1. Diagnosis Here is where your presentation begins

2.   A reaction to diagnosis can surely be very different.   Have  a look at the  reactions  below and try to guess what  diagnosis  the doctor has stated in each case. 01 04 02 05 03 06 Is that  because I  eat too much sugar? Oh no, I hoped it  was  just a common cold … It can't be it. I'm too  fat ! I am 45 kilos,  and  I need to be 40. Once you've  remove the  stones, can I  keep  them?  Oh no, I don't want my leg in a cast! No wonder. With  my job ,  I  can't eat properly. I  live on  sandwiches and  snacks .

3. Nostalgia Do you remember the first time you  diagnosed  a patient? Was it difficult? Why/why not? What difficulties do you face when explaining  a  diagnosis to a patient? What should a doctor do so that patients don't  react  inadequately?

4. Awesome words - bad   examples

5. Phrases to introduce the delivery of diagnosis Your test results have come in. Phrases to state the diagnosis You have (name of illness/condition). You are suffering from (name of illness/condition). You've developed (name of illness/condition). We've ruled out (name of illness/condition). —Let’s practice

6. Use this inf ormation You   think   that   the   patient   has   food   poisoning .  Patient experiences a health problem You   need   to   explain   to   the   patient   that   since   he   hasn't   taken   any   measures ,  his   rhinitis   has   become   chronic   over   the   years .  The   patient's   blood   test   results   are   ready .  The   patient's   blood   test   results   are   ready .  You   don't   have  a  definitive   diagnosis ,  but   it's   not asthma   for   sure . 

7. A doctor has to make sure that the  patient  understands the diagnosis correctly.  Layman's   terms, or  lay terminology, helps to avoid  misunderstandings .

8. Name these medical terms in layman's  terms Cerebrovascular accident Influenza MI (myocardial infarction) Hepatoma Anorexia Cerumen impaction Asthma Adenopathy M igraine Edema Fracture Hypertension 1 2 3 5 6 4

9. Don’t panic When patients hear a diagnosis ,  they might panic. It's very  important  to reassure them right away. What would you say to  reassure a patient  who is recovering from  an illness  but is still feeling weak?

10. Let’s reassure a patient Reassuring phrases Doctor, please I understand your concerns. Your concerns are absolutely natural. There's nothing to worry about/to be  worried about/to be alarmed about You'll be fine./It'll be fine. It'll turn out all right. It isn't as bad as all that. Whatever you may have heard ... Rest assured that .../I can assure you that.. Doctor, maybe it's foolish, but I'm afraid to  undergo surgery. Doctor, I don't believe I will ever stop coughing. Doctor, I'm afraid this illness has become chronic. Doctor, I've read on the Internet that fatigue is one of  the symptoms of cancer. I've been feeling tired  recently… Doctor, my temperature is always 36.7 C. I'm worried! Doctor, I've heard from a blogger that the medication  I'm taking can cause infertility.

11. It usually takes a week before full energy level returns . You have a long road to recovery . You're not in the clear yet . You'll recover fully without a trip to the hospital. Listen to a patient What would you say?

12. Usually , recovery takes (a couple of days/a week/several months). On average, a full recovery period usually takes (a couple of days/a week/several months). It usually takes (a couple of days/a week/several months) to recover / before full energy  level  returns. We would expect you to (have no symptoms / not feel limited) in (a  couple  of days/a week/several months). You'll recover fully without a trip to the hospital. I'm afraid the prognosis isn't good. Hopefully, we can (cure it/expect full recovery/…) You have a long road to recovery. You're not in the clear yet. We'll know more in a few days. You need more tests./ I'll refer you to  another  specialist. I'm hoping to get to the bottom of this soon. We can never be absolutely certain about ... Phrases to talk about prognosis

13. Give a prognosis to the  patients with the  conditions below.   Flu G astritis C ommon  cold H igh  blood pressure Migraine Cancer MI Fracture Pregnancy

14. go to hospital — to be admitted as a patient go to the hospital — visit a hospital (as a visitor, not a patient) In American English, there is no difference,  and  the collocation is always used with "the". Mind the difference

15. Concept of the diagnostic process 1 Committee on Diagnostic Error in Health Care; Board on Health Care Services; Institute of Medicine; The National Academies of Sciences, Engineering, and Medicine; Balogh EP, Miller BT, Ball JR, editors. Improving Diagnosis in Health Care. Washington (DC): National Academies Press (US); 2015 Dec 29. 2, The Diagnostic Process. Available from: https://www.ncbi.nlm.nih.gov/books/NBK338593/

16. Do you need a longer text? Speaking of craters, many of them were named after artists or authors who made significant contributions to their respective fields. Mercury takes a little more than 58 days to complete its rotation, so try to imagine how long days must be there! Since the temperatures are so extreme, albeit not as extreme as on Venus, and the solar radiation is so high, Mercury has been deemed to be non-habitable for humans

17. IMPORTANT CONSIDERATIONS IN THE DIAGNOSTIC PROCESS The committee elaborated on several aspects of the diagnostic process which are discussed below, including: diagnostic uncertainty time population trends diverse populations and health disparities mental health

18. Models of Clinical Reasoning Analytical models (slow system 2 ) Hypothetico-deductivism Clinicians formulate alternative diagnostic possibilities. Clinicians obtain contextual information by taking a history, performing a physical examination, administering diagnostic tests, or consulting with other clinicians . 1.Cue acquisition 2.Hypothesis generation (working diagnoses):

19. Here are 2 important ideas Clinicians interpret the consistency of the information with each of the alternative hypotheses under consideration . The data are weighed and combined to evaluate whether one of the working diagnoses can be confirmed. If not, further information gathering, hypothesis generation, interpretation, and evaluation is conducted until verification is achieved ( Elstein and Bordage , 1988 ). 3.Cue interpretation (diagnostic modification and refinement): 4.Hypothesis evaluation (diagnostic verification):

20. Broadly construed through a pattern-recognition framework, nonanalytical models attempt to understand clinical reasoning through human categorization and classification practices. These models suggest that clinicians make diagnoses and choose treatments by matching presenting patients to their mental models of diseases (or information about diseases that is stored in memory). Although the nature of these mental models remain under debate, most assume that they are either exemplars (specific patients seen previously and stored in memory as concrete examples) or prototypes (an abstract disease conceptualization that weighs disease features according to their frequency) ( Bordage and Zacks , 1984 ;  Norman, 2005 ;  Rosch and Mervis , 1975 ;  Schmidt et al., 1990 ;  Smith and Medin , 1981 ,  2002 ). Expert pattern matching by experienced clinicians may involve illness scripts, in which elaborated disease knowledge includes enabling conditions or risk factors (e.g., physical contact with the Ebola virus); the pathophysiology of the disease (virus replication, invasion and destruction of endothelial surfaces); and the signs and symptoms of the disease ( bleeding) ( Boshuizen and Schmidt, 2008 ). After encountering a patient, a clinician may activate a single illness script or multiple scripts. Illness scripts differ from exemplars and prototypes by having more extensive knowledge stored for each disease. As the diagnostic process evolves, the clinician matches the activated scripts against the presenting signs and symptoms, with the best matching script offered as the most likely diagnosis. While exemplars, prototypes, and illness scripts are assumed to encode different types of information about disease conditions—that is, actual instances versus typical presentation versus multidimensional information—pattern recognition models assume them to play the same role in diagnosis. Nonanalytical models (fast system 1).

21. Examples of Heuristics and Biases That Influence Decision Making Heuristic or Bias Medical Example Nonmedical Example Anchoring   is the tendency to lock onto salient features in the patient's initial presentation and failing to adjust this initial impression in the light of later information. A patient is admitted from the emergency department with a diagnosis of heart failure. The hospitalists who are taking care of the patient do not pay adequate attention to new findings that suggest another diagnosis. We buy a new car based on excellent reviews and tend to ignore or downplay negative features that are noticed. Affective bias  refers to the various ways that our emotions, feelings, and biases affect judgment. New complaints from patients known to be “frequent flyers” in the emergency department are not taken seriously. We may have the belief that people who are poorly dressed are not articulate or intelligent. Availability bias  refers to our tendency to more easily recall things that we have seen recently or things that are common or that impressed us. A clinician who just recently read an article on the pain from aortic aneurysm dissection may tend toward diagnosing it in the next few patients he sees who present with nonspecific abdominal pain, even though aortic dissections are rare. Because of a recent news story on a tourist kidnapping in Country “A,” we change the destination we have chosen for our vacation to Country “B.” Context errors  reflect instances where we misinterpret the situation, leading to an erroneous conclusion. We tend to interpret that a patient presenting with abdominal pain has a problem involving the gastrointestinal tract, when it may be something else entirely: for example, an endocrine, neurologic or vascular problem. We see a work colleague picking up two kids from an elementary school and assume he or she has children, when they are instead picking up someone else's children. Search satisficing ( premature closure ) is the tendency to accept the first answer that comes along that explains the facts at hand, without considering whether there might be a different or better solution. The emergency department clinician seeing a patient with recent onset of low back pain immediately settles on a diagnosis of lumbar disc disease without considering other possibilities in the differential diagnosis. We want a plane ticket that costs no more than $1,000 and has no more than one connection. We perform an online search and purchase the first ticket that meets these criteria without looking to see if there is a cheaper flight or one with no connections.

22. Clinical reasoning is based on the dual process theory The dual process theory integrates analytical and non-analytical models of decision making involve a conscious, deliberate process guided by critical thinking ( Kahneman , 2011 ). involve unconscious, intuitive, and automatic pattern recognition ( Kahneman , 2011 ). Dual theory –widely accepted paradigm of decision making. Analytical models (slow system 2) Nonanalytical models (fast system 1)

23. Breaking bad news is easier for  an  experienced  doctor .  Breaking bad news is completely  different   from  just stating a diagnosis.  Always break bad news to a  patient when  no one  else  is present.  Cross your arms to look  more  serious when  delivering  bad news.  Avoid eye contact with the patient  -  it will make things easier.  Don't say bad news without a  warning .  Don't delay delivering bad news.  The pauses are important when you deliver bad  news .  It's okay to hide some truth when  answering  the  patient's  questions.  Breaking bad news

24. The dual process model of diagnostic decision making. When a patient presents to a clinician, the initial data include symptoms and signs of disease, which can range from single characteristics of disease to illness scripts.

25. Calibration in the diagnostic process

26. Clinical case A woman has a 0.8 percent risk of having breast cancer. Among women with breast cancer, a mammogram will be positive in 90% (sensitivity). Among women without breast cancer, a mammogram will be positive in 7% (false positive rate or 1 minus a specificity of 93%). If the mammogram is positive, what is the likelihood of this woman having breast cancer? Bayes ' rule provides the answer. Among 1,000 women, 8 (0.8 % of 1,000) will have breast cancer and about 7 (90 % of 8) would have a true positive mammogram. Among the 992 without breast cancer, 69 (7 % of 992) will have a false positive mammogram. Thus, among the 76 women with a positive mammogram, 7—or 9%—will have breast cancer. When a very similar question was presented to practicing physicians with an average of 14 years of experience, their answers ranged from 1% to 90%, and very few answered correctly ( Gigerenzer and Edwards, 2003 ).

27. THE DIAGNOSTIC EVIDENCE BASE AND CLINICAL PRACTICE With the rapidly increasing number of published scientific articles on health, health care professionals have difficulty keeping up with the breadth and depth of knowledge in their specialties. For example, to remain up to date, primary care clinicians would need to read for an estimated 627.5 hours per month. Thus , clinicians need approaches to ensure they know the evidence base and are well-equipped to deliver care that reflects the most up-to-date information. One of the ways that this is accomplished is through team-based care; by moving from individuals to teams of health care professionals, patients can benefit from a broader set of resources and expertise to support care ( Gittell et al., 2010 ) In addition, systematic reviews and clinical practice guidelines (CPGs) help synthesize available information in order to inform clinical practice decision making ( IOM, 2011a , b ).

28. AI, MAGIC WAND? Computation can be used to analyze images. Subtypes of machine learning, such as convolutional neural networks, “can identify subtle changes in chest X-ray films, and in some instances, the accuracy levels for diagnosing conditions , such as pneumonia, are equivalent or superior to that of clinicians ,”. “ Unlike traditional statistical methods, machine-learning algorithms mimic human cognitive processes when making decisions .” In April 2018, the US Food and Drug Administration  approved  the first AI-based diagnostic, IDx -DR, which detects diabetic retinopathy in people with diabetes by analyzing retinal images. Machine learning will soon be applied to many other medical conditions, from  cardiology  to  neurodegenerative diseases  and beyond.

29. Improving prognostics and pateint monitoring In addition to using it to diagnose conditions, clinicians can use machine learning to predict a patient’s prognosis. For example, one international team of scientists developed a machine learning–based tool that analyzes the prognosis of patients with stage III colon cancer, and the group  reported  that the results “could provide crucial information to aid treatment planning” for people with this disease. Plus , John Halamka , president of the Mayo Clinic Platform, and his colleagues  suggested  that machine learning might improve a clinician’s ability to determine the likely outcome of a patient with COVID-19. As with the use of machine language in clinical diagnosis, work in prognosis promises many improvements ahead . One day, machine learning and wearable technology could continuously monitor a person’s health. “Two of the most commercially available AI systems are incorporated in devices like the Apple Watch or the Kardia Alivecor devices, which can detect arrhythmias and send alerts to patients through their smartphone apps. AI will likely have a big impact in cardiology, cancer, and neurosciences by helping stratify and profile patients, enabling more proactive management and care .

30. Let’s discuss What is the  most   important thing   when breaking   bad   news  in your opinion? What other tips do  you have  to add? Do the doctors feel  stressedwhen   delivering bad  news ?  How can  they cope  with stress? ”

31. B G H A I am afraid I  have  some bad news for you. Phrases   to   break   bad   news Bad news I can see this is a huge shock for you. Would you prefer  to  have a  family  member or friend here? I can see how upsetting this is for you. I have the result  here  today, would you like me to explain it to you now? I'm afraid it's not the  news  we were  hoping  for. I am so sorry to tell you, but ... I was also hoping for a better result. C E D F I I can see that this is not the news that you expected, I'm so sorry.

32. Let’s role-play Role 1 Doctor Brown Needs to tell about cancer Role 2 Patient Completed checkup I am afraid I have  some  bad news for you. Oh ,  bad   news   …   I   didn't   expect   it . I have the result here today, would you like me to explain it to you now? Yes ,  let's   not   delay   it . Doctor Patient Doctor Patient

33. This is a map Follow the link in the map to modify its data and then paste the new one here. For more info, click here Venus Venus is very hot Mercury Mercury is small Mars Mars is very cold

34. A timeline always works well Mercury is the closest planet to the Sun Mercury 03 Saturn Saturn was named after a Roman god 04 Mars Despite being red, Mars is a cold place 07 Jupiter Jupiter was named after a Roman god 06 Ceres Ceres is located in the main asteroid belt 05 02 The Moon is Earth’s only natural satellite Moon Earth Earth is the third planet from the Sun 01 Neptune Neptune is the farthest planet from the Sun 08

35. Use tables to represent data Team A Team B Team C Team D Team E Team F Mercury XX XX XX XX XX XX Mars XX XX XX XX XX XX Saturn XX XX XX XX XX XX Venus XX XX XX XX XX XX Jupiter XX XX XX XX XX XX Earth XX XX XX XX XX XX Moon XX XX XX XX XX XX

36. You can use this graph Follow the link in the graph to modify its data and then paste the new one here. For more info, click here 20XX Venus is very hot 20XX Mercury is small 20XX Mars is very cold

37. One last diagram 1 Earth is the third planet from the Sun Earth 2 Despite being red, Mars is a cold place Mars 3 Venus is the second planet from the Sun Venus Study of the image Saturn was named after the Roman god of wealth and agriculture

38. Tom Harris Sofia Hill Our team You can speak a bit about this person here You can speak a bit about this person here

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